MAE 589 Fundamentals for Modern Robotics

This class will be offered in Fall 2025.

Discription:

Robotic systems have increasingly integrated planning and control algorithms to operate effectively in the real world. Legged robots, such as quadrupedal robots and bipedal humanoid robots, are designed to navigate challenging terrains and perform a wide range of loco-manipulation tasks, including autonomous inspection, transportation, and more. This course covers the fundamental principles of planning and control for legged robots. Topics include kinematic and dynamic modeling, floating-base dynamics, contact dynamics, hierarchical control frameworks, whole-body control, locomotion control, and trajectory generation techniques. These concepts will be illustrated with practical examples from quadrupedal and bipedal robots. The course emphasizes practical and applied approaches to planning and control, focusing on quadrupedal and humanoid robots.

Format:

The course will include two weekly lectures, four homework assignments, and a final project. The lectures will cover fundamental concepts such as rigid-body dynamics, optimal control theory, and planning and control techniques for modern robotics, with a particular focus on methods applicable to legged robots, including humanoid and animaloid robots. The assignments will emphasize practical implementation, requiring students to do simulation of locomotion gaits and solve coding problems related to the covered topics. For the final project, students will define their own problem and develop at least one solution using simulation tools.

Prerequisites:

Dynamics Class, e.g., MAE 208: Engineering Dynamics
Control Class, e.g., MAE 435: Principles of Automatic Control

Lecture Slides and Videos:

Lecture slides will be posted on the course website one hour before each lecture. For students enrolled in the course, recorded lecture videos will be posted after each lecture.

Timeline:

Date	Lecture	Deadlines	References
Week 1	Course introduction
Week 2	Terminology and Rigid-body Motion		Modern robotics, Lynch et al.(2017) Rigid body dynamics algorithm, Featherstone (2008)
Week 3	Kinematic Control	Project Survey	Modern robotics, Lynch et al.(2017) Intermediate Desired Value Approach for Task Transition of Robots in Kinematic Control, Lee et al.(2012)
Week 4	Dynamic Control	Homework 1	Modern robotics, Lynch et al.(2017) Rigid body dynamics algorithm, Featherstone (2008)
Week 5	Floating-base and Contact Dynamics	Project Proposal	Centroidal dynamics of a humanoid robot, Orin et al.(2013) Robot multiple contact control, Park et al.(2008)
Week 6	Optimization-based Control		Hierarchical quadratic programming: Fast online humanoid-robot motion generation, Adrien et al.(2014)
Week 7	Hierarchical Task-space Control	Homework 2	Continuous task transition approach for robot controller based on hierarchical quadratic programming, Kim et al. (2019) Intermediate Desired Value Approach for Task Transition of Robots in Kinematic Control, Lee et al.(2012)
Week 8	Whole-body Control		Whole-body dynamic behavior and control of human-like robots, Khatib et al.(2004) Synthesis of whole-body behaviors through hierarchical control of behavioral primitives, Sentis et al.(2005) Dynamic locomotion for passive-ankle biped robots and humanoids using whole-body locomotion control, Kim et al.(2020) Online gain adaptation of whole-body control for legged robots with unknown disturbances, Lee et al.(2022)
Week 9	Reduced-order Models	Project milestone	Double Integrator, Unicycle, Single Rigid Body Models Linear Inverted Pendulum (LIP), Hybird LIP (HIP), Angular-momentum-based LIP (ALIP)
Week 10	Locomotion Gaits	Homework 3	Quadrupal locomotion: Trot gait Bipedal locomotion: Dynamic walking
Week 11	Model-free Path Planning		Rapidly-exploring ramdom tree (RRT), RRT*
Week 12	Data-driven Planning	Homework 4	Monte-Carlo method Simple Examples of Neural Network
Week 13	No class	Final project presentation/report

Project requirements:

You need to choose a proper high-fidelity simulation platform such as one of the following platforms:
Mujoco:https://github.com/google-deepmind/mujoco
Gezebo:https://classic.gazebosim.org/
Bullet:https://pybullet.org/wordpress/
Issac Lab:https://github.com/isaac-sim/IsaacLab

Grading and Course Policies: